Flow-Based Models

Flow-based generative models use invertible transformations to map complex probability distributions to simpler ones. Unlike GANs and VAEs, which often struggle with training stability, flow-based models ensure exact likelihood estimation, making them highly reliable for high-fidelity image and video synthesis.

Popular examples include RealNVP and Glow, which have been used for generating high-resolution images and video effects. Their ability to generate sharp, detailed images without adversarial training makes them an alternative approach to traditional generative modeling.